Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state model output and the current state model of the open loop model, the estimate state module determining estimator gain associated with the current state model and applying the estimator gain to determine the estimated state of the model.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A control system, comprising: an actuator for positioning a control device comprising a control surface, wherein the actuator positions the control surface; a control law for directing the actuator as a function of a model output; and a model processor for generating the model output, the model processor comprising: an input object for processing model input and setting a model operating mode; a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode; wherein the open loop model generates current state derivatives, solver state errors, and synthesized parameters as a function of the dynamic states and the model input, wherein a constraint on the current state derivatives is based on a series of cycle synthesis modules interacting in accordance to laws of physics governing the controlled apparatus behavior, each member of the series of cycle synthesis modules modeling a component of a cycle of the control device and comprising a series of utilities, the utilities based on mathematical abstractions of physical laws that govern behavior of the component; an estimate state module for determining an estimated state of the model based on at least one of a prior state of the model, the current state derivatives, the solver state errors, and the synthesized parameters, the estimate state module determining estimator gain associated with the current state derivatives and applying the estimator gain to determine the estimated state of the model, wherein determining estimator gain associated with the current state model and applying the estimator gain is achieved by using a fixed structure with a configurable architecture; and an output object for processing at least the synthesized parameters of the model to determine the model output.
2. The control system of claim 1 , wherein determining estimator gain includes employing multidimensional gain base point interpolation.
3. The control system of claim 2 , wherein the multidimensional gain base point interpolation includes using at least three scheduling parameters.
4. The control system of claim 1 , wherein the configurable architecture comprises a first leg for scaling and correcting an error input using scaling and correcting factors.
5. The control system of claim 4 , wherein the configurable architecture further comprises: a second leg for selecting groups of vectors from the error input for gain determination; and a third leg for determining and applying gain to the groups of vectors.
6. The control system of claim 5 , wherein the configurable architecture further comprises a fourth leg for unsealing and uncorrecting the groups of vectors.
7. The control system of claim 6 , wherein the configurable architecture further comprises a fifth leg for assigning values to output vectors of the estimated state of the model.
8. The control system of claim 1 , wherein the control device is a gas turbine engine.
9. A method for controlling a control device, the method comprising: generating a model output using a model processor, the model processor comprising: an input object for processing model input and setting a model operating mode; a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode; wherein the open loop model generates current state derivatives, solver state errors, and synthesized parameters as a function of the dynamic states and the model input, wherein a constraint on the current state derivatives is based on a series of cycle synthesis modules interacting in accordance to laws of physics governing the controlled apparatus behavior, each member of the series of cycle synthesis modules modeling a component of a cycle of the control device and comprising a series of utilities, the utilities based on mathematical abstractions of physical laws that govern behavior of the component; an estimate state module for determining an estimated state of the model based on at least one of a prior state of the model, the current state derivatives, the solver state errors, and the synthesized parameters, the estimate state module determining estimator gain associated with the current state derivatives and applying the estimator gain to determine the estimated state of the model, wherein the determining estimator gain associated with the current state model and applying the estimator gain is achieved by using a fixed structure with a configurable architecture; and an output object for processing at least the synthesized parameters of the model to determine a model output; directing an actuator associated with the control device as a function of the model output using a control law; and positioning the control device comprising a control surface using the actuator, wherein the actuator positions the control surface.
10. The method of claim 9 , wherein determining estimator gain includes employing multidimensional gain base point interpolation.
11. The method of claim 10 , wherein the multidimensional gain base point interpolation includes using at least three scheduling parameters.
12. The method of claim 9 , wherein the configurable architecture comprises a first leg for scaling and correcting an error input using scaling and correcting factors.
13. The method of claim 12 , wherein the configurable architecture further comprises: a second leg for selecting groups of vectors from the error input for gain determination; and a third leg for determining and applying gain to the groups of vectors.
14. A gas turbine engine comprising: a fan; a compressor section downstream of the fan; a combustor section downstream of the compressor section; a turbine section downstream of the combustor section; an actuator for positioning the gas turbine engine, wherein the actuator positions a control surface of an element of the gas turbine engine; a control law for directing the actuator as a function of a model output; a model processor for generating the model output, the model processor comprising: an input object for processing model input and setting a model operating mode; a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode; wherein the open loop model generates a-current state derivatives, solver state errors, and synthesized parameters as a function of the dynamic states and the model input, wherein a constraint on the current state derivatives is based on a series of cycle synthesis modules interacting in accordance to laws of physics governing the controlled apparatus behavior, each member of the series of cycle synthesis modules modeling a component of a cycle of the gas turbine engine and comprising a series of utilities, the utilities based on mathematical abstractions of physical laws that govern behavior of the component; an estimate state module for determining an estimated state of the model based on at least one of a prior state of the model, the solver state errors, the current state derivatives of the open loop model, and the synthesized parameters, the estimate state module determining estimator gain associated with the current state derivatives and applying the estimator gain to determine the estimated state of the model, wherein determining estimator gain associated with the current state model and applying the estimator gain is achieved by using a fixed structure with a configurable architecture; and an output object for processing at least the synthesized parameters of the model to determine the model output.
15. The gas turbine engine of claim 14 , wherein determining estimator gain includes employing multidimensional gain base point interpolation.
16. The gas turbine engine of claim 15 , wherein the multidimensional gain base point interpolation includes using at least three scheduling parameters.
17. The gas turbine engine of claim 14 , wherein the configurable architecture comprises: a first leg for selecting groups of vectors from the error input for gain determination; and a second leg for determining and applying gain to the groups of vectors.
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March 14, 2014
October 23, 2018
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